1. Small Area Estimation with application to Urban Audit project estimates
Dr. Eng. H. Omrani and Dr. P. Gerber
GEODE department, CEPS/INSTEAD, LU
EUROSTAT, sept., 2010

2. Outline 1. Introduction 2. Proposition 3. Conclusions and perspectives
Objectives and problems
Overview: Small Area Estimation
2. Proposition
Theoritical aspects
Application, results and assessment
3. Conclusions and perspectives

3. 1. Introduction

4. Small Area Estimation
Methods: Horvitz Thomson estimator, GREG, EBLUP, S- EBLUP,
LOGIT and NN

5. How do we get the data?

6. The variables to be estimated:
Total Number of Households (excluding institutional households)
One person households
Households with children aged 0 to under 18
Total Number of Households with less than half of the national average disposable annual household income

7. Outline 1. Introduction 2. Proposition 3. Conclusions and perspectives
Objectives and problems
Overview: Small Area Estimation
2. Proposition
Theoritical aspects
Application and results
Assessment
3. Conclusions and perspectives

8. 2. Proposition LOGIT NEURAL NETWORK Sensitivity analysis:
ROC curve analysis
Monte Carlo Simulation

9. Application: Number of households with level estimation (city level)
The data used is from:
the census file (2001)
administrative data sets ( )
The variables used:
Fig: Multi-layer perceptron network for prediction of number of households

10. Results from Logit and Neural Network models

11. Results…

12. 3. Conclusions
Neural Network (NN) is particularly useful and efficient when complex nonlinear phenomena.
NN technique seems particularly adapted to estimate sublevel indicators better than Logit model.
The results of the proposed methods are assessed by ROC curve analysis
They have been represented by confidence interval with Monte Carlo simulation.
In the future, we plan to apply the developed technique to estimate the set of indicators at sublevel scale of the UA project.
In a next step of our research, we may try to :
include the spatial units (e.g. municipality level) into the NN model
predict the revenues and the structure of households (number of children)
compare the results with Poisson regression model which is widely used to predict such variables.

13. Thanks for your attention QUESTIONS !